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Balancing Speed and Security: Reconciling AML, Sanctions, and Real-Time Payments
At the Inclusive Fintech Forum in Rwanda, FNA's Kimmo Soramäki highlighted the critical challenge of securing booming real-time payment networks against escalating financial crime without sacrificing speed or financial inclusion. The panel emphasized that overcoming the limitations of siloed compliance requires a shift toward coordinated, network-wide intelligence and collaborative national anti-scam infrastructures.
Fraud vs Scams: The Architectural Shift Required to Protect Real-Time Payments
As financial crime rapidly shifts from unauthorized breaches to Authorized Push Payment (APP) scams, traditional siloed transaction monitoring systems are failing to detect malicious networks. To counter the speed of real-time payments, the industry must transition to a collaborative, network-first approach leveraging Graph AI to trace mule clusters and create systemic immunity across the entire financial ecosystem.
Summary of Payment Systems Broadcast #25: Building Trust at Speed:
Dr. Kimmo Soramäki (FNA) and Curtis Matlock (Proto) reveal how their G20 TechSprint-winning solution uses multilingual AI and real-time graph analytics to dismantle "industrial-scale" scams.
Summary Payment Systems Broadcast #24: Tackling Public Sector Fraud
Mark Cheeseman OBE (PSFA) and Phillip Straley (FNA) examine the UK’s "cross-system" defense against public-sector fraud, highlighting how the Single Network Analytics Platform (SNAP) is uncovering hidden connections in minutes.
FNA and Proto Shortlisted for G20 TechSprint 2025 with Breakthrough Anti-Scam Centre
FNA and Proto join forces for the G20 TechSprint 2025, presenting "Trust at Speed"—a national utility that integrates multilingual AI reporting with real-time graph analytics to close the "fraud response gap."
Following the Money: How Legal Entity Identifiers Illuminate Fraud Trails
As fraud outpaces traditional compliance, central banks are shifting toward unified national infrastructures that leverage AI and real-time tracing to proactively protect consumers and restore financial system integrity.
Multi-Rail, Multi-Bank Approaches to Payment Fraud Detection
FNA utilizes Graph Machine Learning instead of Federated Learning for real-time payment fraud detection because centrally analyzing hashed, multi-bank data reveals coordinated illicit networks and significantly increases predictive performance.
A Letter From – Hong Kong & Tokyo
An executive recap of key financial trends in Asia, highlighting regional progress in fraud prevention, the surge in offshore Renminbi business, and the modernization of national payments infrastructure.